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---
tags:
- moe
- fp8
- vllm
license: other
license_name: deepseek-license
base_model: deepseek-ai/DeepSeek-Coder-V2-Base
library_name: transformers
---
# DeepSeek-Coder-V2-Instruct-0724-FP8
## Model Overview
- **Model Architecture:** DeepSeek-Coder-V2-Instruct-0724
- **Input:** Text
- **Output:** Text
- **Model Optimizations:**
- **Weight quantization:** FP8
- **Activation quantization:** FP8
- **Release Date:** 3/1/2025
- **Version:** 1.0
- **Model Developers:** Neural Magic
Quantized version of [DeepSeek-Coder-V2-Instruct-0724](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Instruct-0724).
### Model Optimizations
This model was obtained by quantizing weights and activations to FP8 data type, ready for inference with vLLM >= 0.5.2.
This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. Only the weights and activations of the linear operators within transformers blocks are quantized, except the MLP routers.
## Deployment
### Use with vLLM
This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.
```python
from transformers import AutoTokenizer
from vllm import LLM, SamplingParams
max_model_len, tp_size = 4096, 4
model_name = "neuralmagic-ent/DeepSeek-Coder-V2-Instruct-0724-FP8"
tokenizer = AutoTokenizer.from_pretrained(model_name)
llm = LLM(model=model_name, tensor_parallel_size=tp_size, max_model_len=max_model_len, trust_remote_code=True)
sampling_params = SamplingParams(temperature=0.3, max_tokens=256, stop_token_ids=[tokenizer.eos_token_id])
messages_list = [
[{"role": "user", "content": "Who are you? Please respond in pirate speak!"}],
]
prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True) for messages in messages_list]
outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params)
generated_text = [output.outputs[0].text for output in outputs]
print(generated_text)
```
vLLM also supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
## Creation
This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below with the following command:
```bash
python quantize.py --model_path deepseek-ai/DeepSeek-Coder-V2-Instruct-0724 --quant_path "output_dir" --calib_size 128
```
```python
import argparse
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer
from llmcompressor.modifiers.quantization import QuantizationModifier
from llmcompressor.transformers import oneshot
from llmcompressor.transformers.compression.helpers import calculate_offload_device_map
import torch
import os
def main():
# Set up command line argument parsing
parser = argparse.ArgumentParser(description='Quantize a transformer model to FP8')
parser.add_argument('--model_id', type=str, required=True,
help='The model ID from HuggingFace (e.g., "meta-llama/Meta-Llama-3-8B-Instruct")')
parser.add_argument('--save_path', type=str, default='.',
help='Custom path to save the quantized model. If not provided, will use model_name-FP8')
parser.add_argument('--calib_size', type=int, default=256)
args = parser.parse_args()
device_map = calculate_offload_device_map(
args.model_id,
reserve_for_hessians=False,
num_gpus=torch.cuda.device_count(),
trust_remote_code=True,
torch_dtype=torch.bfloat16,
)
model = AutoModelForCausalLM.from_pretrained(
args.model_id, device_map=device_map, torch_dtype=torch.bfloat16, trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(args.model_id)
NUM_CALIBRATION_SAMPLES = args.calib_size
DATASET_ID = "garage-bAInd/Open-Platypus"
DATASET_SPLIT = "train"
ds = load_dataset(DATASET_ID, split=DATASET_SPLIT)
ds = ds.shuffle(seed=42).select(range(NUM_CALIBRATION_SAMPLES))
def preprocess(example):
concat_txt = example["instruction"] + "\n" + example["output"]
return {"text": concat_txt}
ds = ds.map(preprocess)
def tokenize(sample):
return tokenizer(
sample["text"],
padding=False,
truncation=False,
add_special_tokens=True,
)
ds = ds.map(tokenize, remove_columns=ds.column_names)
# Configure the quantization algorithm and scheme
recipe = QuantizationModifier(
targets="Linear", scheme="FP8", ignore=["lm_head", "re:.*\.mlp\.gate$"]
)
# Apply quantization
oneshot(
model=model,
dataset=ds,
recipe=recipe,
num_calibration_samples=args.calib_size
)
save_path = os.path.join(args.save_path, args.model_id.split("/")[1] + "-FP8")
os.makedirs(save_path, exist_ok=True)
# Save to disk in compressed-tensors format
model.save_pretrained(save_path, save_compressed=True, skip_compression_stats=True)
tokenizer.save_pretrained(save_path)
print(f"Model and tokenizer saved to: {save_path}")
if __name__ == "__main__":
main()
```
## Evaluation
The model was evaluated on [HumanEval and HumanEval+](https://github.com/openai/human-eval?tab=readme-ov-file) benchmark with the [Neural Magic fork](https://github.com/neuralmagic/evalplus) of the [EvalPlus implementation of HumanEval+](https://github.com/evalplus/evalplus) and the [vLLM](https://docs.vllm.ai/en/stable/) engine, using the following commands:
```
python evalplus/codegen/generate.py --model neuralmagic-ent/DeepSeek-Coder-V2-Instruct-0724-FP8 --bs 16 --temperature 0.2 --n_samples 50 --root "./results" --dataset humaneval --backend vllm --dtype auto --tp 8
python evalplus/evalplus/sanitize.py results/humaneval/neuralmagic-ent--DeepSeek-Coder-V2-Instruct-0724-FP8_vllm_temp_0.2
evalplus.evaluate --dataset humaneval --samples results/humaneval/neuralmagic-ent--DeepSeek-Coder-V2-Instruct-0724-FP8_vllm_temp_0.2-sanitized
```
### Accuracy
#### HumanEval evaluation scores
| Metric | deepseek-ai/DeepSeek-Coder-V2-Instruct-0724 | neuralmagic-ent/DeepSeek-Coder-V2-Instruct-0724-FP8 |
|------------------------|:---------------------------------:|:-------------------------------------------:|
| HumanEval pass@1 | 89.3 | 88.7 |
| HumanEval pass@10 | 93.1 | 92.9 |
| HumanEval+ pass@1 | 82.9 | 82.8 |
| HumanEval+ pass@10 | 87.6 | 86.9 |
| **Average Score** | **88.23** | **87.83** |
| **Recovery** | **100.00** | **99.55** |
|